BlackRock Is Making Big Data Bigger

Rob Goldstein, chief operating officer and global head of
BlackRock Solutions, and Jody Kochansky, head of the product
group for Aladdin, the firms portfolio management system,
cant describe the future of data science at the $5.1
trillion money manager without comparing their ideas to the way
things worked in the past.

Analyzing data is not new to the two executives. During a
discussion Kochansky and Goldstein constantly interrupt each
other in a lively back-and-forth that touches on robots,
artificial intelligence, and machine learning. Both men started
their careers at BlackRock a little more than two decades ago
working on the iconic Green Package, the suite of risk
analytics reports that took its name from the color of the only
copy paper available in the building one night shortly after
the firms founding in 1988. Big data is not about
breakthroughs with the math or methods, says Goldstein,
hired by Kochansky in 1994 as an analyst straight out of
college. Its the data now available and the
computing power to analyze that data. If you were to put us in
a time machine and send us back 20 years, we would tell you
that what youre talking about being able to now do with
data was not possible.

Kochansky, who oversees more than 1,500 developers for
Aladdin, BlackRocks central nervous system, compares now
with then in the mortgage market. In its early days the firm
predicted how quickly homeowners would prepay their loans
 an essential question for a money manager founded on its
expertise in complex mortgage securities  based on
information like the average credit score of borrowers in a
bundle of loans. Now Fannie and Freddie are giving you
data that was unimaginable two decades ago about every
mortgage, says Kochansky. He stresses, though, that
its not only about the breadth and volume of data:
Its about the science of models, including measuring
their accuracy, fitting them within the current economy, and
analyzing interrelated data sets. You really need big
data to wrestle this to the ground, he adds.
Kochanskys 24 years at the firm have included a number of
stints working closely with Goldstein, one of them building the
Aladdin business from 1998 to 2007 and another rebuilding the
platform after the 2009 acquisition of Barclays Global
Investors.

Although Goldstein and Kochansky dont say it out loud,
their work finding new breakthroughs in the mature business of
investing is essential to maintaining BlackRocks premier
spot in the industry. Other big money managers and hedge funds
are gearing up data science efforts, but BlackRocks edge
is in Aladdin, which is a shared repository for all of the
firms insights and ideas.

Of course the money management industry has always relied on
patterns to pick stocks. But now that individuals and
businesses have generated billions of pieces of data  90
percent of all the data in the world has been created in the
past three years  investors are frantically sifting
through it to discover new leading indicators of the movement
of stock prices. We want to find the data sources that
are not well known but are in fact predictive of future
returns, Kochansky says. Social media activity, satellite
images of big retailers parking lots in developing
markets, and company research reports are just a few examples
of fertile sources of information. BlackRocks job is to
structure that unstructured data into information that can be
played with by data scientists  by, say, creating an
algorithm that counts cars in satellite images at certain dates
and times. Data scientists can then analyze that information
using typical statistical methods  all within
Aladdin.

Goldstein cites a college friend from Binghamton University
who became an equity research analyst and once spent three
months, from November to January, counting cars, customers, and
inventory at malls. In contrast, he says, Raffaele Savi 
co-head of investments in BlackRocks Scientific Active
Equity group  told clients at a recent investor day that
he reads every report every company analyst has ever produced.
Of course its not him. Its the
computer, Goldstein stresses. And as BlackRocks
computers suck in all these analysts reports, regulatory
filings, and other materials, theyre attempting to find
other clues left behind. For example, theyre sorting
through the language in transcripts of earnings calls, looking
for changes in tone and counting positive and negative words.
Naturally, once BlackRock is successful in decoding these
reports, management at those companies will catch on and
attempt to change the signals theyre inadvertently
sending. Although clearly a more prosaic task, such parsing of
clues from regulatory filings brings to mind Alan Turing and
his team of mathematicians who cracked Nazi codes during World
War II  which, once cracked, were used sparingly enough
that Germany never caught on.

Data science is also part of BlackRocks effort to make
the sophisticated analytics and portfolio construction
capabilities of Aladdin available not only to its historical
base of institutional investors but to retail clients through
their financial advisers. The firm offers Aladdin for Wealth
and FutureAdvisor, a digital advice platform it acquired last
year and provides through third parties like RBC Wealth
Management. Goldstein says regulatory changes such as the
Department of Labors fiduciary rule  which mandates
that advisers for retirement accounts act in the best interests
of their clients  and similar global requirements
highlight the benefits of Aladdin capabilities like portfolio
construction and transparency tools in the retail world.
Big-data techniques allow BlackRock to scale Aladdin, offering
its features cost-effectively to millions of retail accounts
and arming its wholesalers, who focus on advisers, with
relevant client information, model portfolios, and trade
ideas.

Operations is another area rife with opportunities for data
scientists. Aladdin carries out 250,000 trades a day and runs
billions of economic forecasts and scenarios each night. The
firm is using machine learning, as an example, to detect
complex patterns in its huge amounts of data on trade activity
in order to determine transactions most likely to fail.

Kochansky recounts another story from early in his career to
explain one more application of machine learning: To find
anomalies, he would put that days Green Package next to
the previous days and manually compare the numbers. If a
risk model had spit out a questionable figure, it meant
tracking down the possible error  for instance, a bad
price  and then rerunning the process. Although BlackRock
has automated these activities, people are still essential to
Aladdins exception-based work flow, where the computer
identifies any problems that need human intervention.

BlackRocks next goal is to teach the computer to fix
the problem as well as identify it. The firm is building
robots, or applications, to make judgments now decided by
humans. Every single time we detect an anomaly today and
a human takes a corrective action, we are creating a giant data
set so the robot can learn what actions are required,
says Kochansky.

Humans arent disappearing anytime soon. BlackRock,
which has long proclaimed that it doesnt solely rely on
computer models, says this ethos will remain despite
advancements in automation. If you look at the research,
it tells you that the human plus computer will
always lead to a better result than computer alone or a human
alone, Goldstein says.

BlackRock is well aware of the competition. Signals that
seem promising today will stop working as others stumble onto
them. If enough analysts can pick up on useful indicators based
on the number of positive or negative words in an earnings
transcript or whether the CEO and CFO were consistently upbeat,
theres no doubt companies will start changing their
behavior. Any edge has a half-life that is generally
pretty short, says Kochansky. It really has always
been an arms race. Yep, everyone wants an Enigma
machine.

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